Triple
T31998901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Thai Navy SEALs |
E817066
|
entity |
| Predicate | casualtyInOperation |
P174434
|
FINISHED |
| Object | Saman Kunan |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Saman Kunan | Statement: [Royal Thai Navy SEALs, casualtyInOperation, Saman Kunan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtyInOperation Context triple: [Royal Thai Navy SEALs, casualtyInOperation, Saman Kunan]
-
A.
battleCasualty
Indicates that an entity was killed, wounded, or otherwise harmed as a direct result of a specific battle or armed conflict.
-
B.
rescueCasualties
Indicates performing actions to locate, assist, and remove injured or endangered individuals from a hazardous or emergency situation.
-
C.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
D.
sustainedHeavyCasualtiesAt
Indicates that an entity experienced a large number of serious losses (e.g., deaths or injuries) at a specific location or during a specific event.
-
E.
casualtiesDescription
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f348f8ce388190ae84376b1f348f12 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c2451e108190a73ccfdc99203d55 |
completed | May 3, 2026, 3:34 a.m. |
| PD | Predicate disambiguation | batch_69f6bd25bed08190befcabd3a41ffadf |
completed | May 3, 2026, 3:12 a.m. |
| PDg | Predicate description generation | batch_69f6c1b666188190ac43c3011a7df048 |
completed | May 3, 2026, 3:32 a.m. |
Created at: May 1, 2026, 12:14 a.m.